Various network-based search applications allow users to enter one or more search terms and in response, receive a list of search results. These systems use numerous different types of ranking algorithms to ensure that both the search results are relevant to the user's query and displayed in a useful way. For example, some systems such as Google Search and Google Map Search rank results based on reliability and safety of the search result, location of the user, etc. In addition, business listings included in search results may be ranked and displayed according to the prominence of the business. For example if the system may determine that the user is searching for a business, the search application may also display a list of prominent (or well known, respected or important) businesses based on the user's location.
Some third parties may attempt to defraud these services in order to misdirect users towards unrelated or fraudulent web sites. Some third parties may submit fake business information to the services by “keyword-stuffing”. For example, a third party hijacker (or a hijack spammer) is a third party that tries to get a particular business identified or associated with another highly prominent business such that the particular business's listing or information is displayed prominently in a list of search result. The highly prominent business may be completely unrelated to the particular business. These hijackers may target important businesses, such as well known restaurants or hotels, and include information about the important business's contact information (such as a phone number) into listing data associated with the particular business. Where the hijacker has inserted the prominent business's title (name) into the title or content of the particular business' listing, the business listing may be considered fraudulent or “keyword-stuffed”. As a result, the particular business's information is identified by these map or web search services, which may associate the particular business listing with the more prominent business and thus display the particular business' listing or information more often in search results which legitimately include the prominent business. For example, FIG. 9 demonstrates a keyword-stuffed business listing where a user has entered the search terms 910, “courtyard 4422 y st marriott sacramento” into a map search. The search results include a business listing 920 for “$28 Locksmith Sacramento.” Assuming the search was looking for a hotel, this business listing would be considered unrelated. However has appeared because the business has included references 930, such as user reviews, the name and address of a Courtyard by Marriott. It is important to note that while the particular business listing is fraudulent or “keyword stuffed”, the particular business itself may or may not be a legitimate business.
Current techniques for identifying fraud include searching for particular words or using language models to identify spam emails or fraudulent meta data in web pages. For example, machine learning classifiers may be trained to identify differences between spam emails and good (non-spam) emails. Some systems may allow users to keep track of “trusted” senders. If a sender is “trusted” the emails received from the sender would also be trusted and not treated as spam. In another example, machine learning classifiers may be trained to identify differences between spam web pages and good web pages, for example by examining the age and contents of the web page.